We have proposed a deep learning approach for event-based human pose estimation from a single event-camera. Our method aggregates events into synchronous tensor representations to feed a multi-stage Convolutional Neural Network. Our architecture predicts three orthogonal heatmaps which are triangulated to obtain the final 3D pose.
We validated our approach on the event-based DHP19 dataset, where it showed satisfactory per-movement performance against DHP19 stereo approach [1]}. Moreover, we proposed Event-Human3.6m, a new dataset of simulated events from the standard Human3.6m [2]}. Event-Human3.6m extends DHP19 with more challenging movements and actions. We conducted experiments on the synthetic dataset and adopted a cross-subject protocol which is comparable to the standard RGB testing. Although we recognize the differences between synthetic and RGB datasets, our proposal achieved an accuracy comparable to RGB approaches. These experiments demonstrated the effectiveness of our method.
